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URL: https://willitrunai.com/can-run/nous-dolphin-13b-on-t4-16gb

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Can Nous Dolphin 13B run on NVIDIA T4 16GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Nous Dolphin 13B needs ~24.4 GB but NVIDIA T4 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 24.4 GB, exceeds 16.0 GB available
24.4 GB required16.0 GB available
153% VRAM needed

8.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.6 tok/s

TTFT

29493 ms

Safe context

5K

Memory

24.4 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights9.4 GB
KV Cache12.2 GB
Runtime1.2 GB
Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsNous Dolphin 13B on NVIDIA T4 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 6.6 tok/s decode · 29.5s TTFT (warm) · 16 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 24.4 GB, but this setup only exposes 16.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBVery compromised (needs ~1.2 GB host RAM)12.4 tok/s8541 ms5K
CodingFToo heavy6.6 tok/s29493 ms5K
Agentic CodingFToo heavy3.4 tok/s82824 ms5K
ReasoningFToo heavy6.6 tok/s34855 ms5K
RAGFToo heavy3.4 tok/s103529 ms5K

Quantization options

How Nous Dolphin 13B (13B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowB70
Q3_K_S
3
6.4 GB
LowA71
NVFP4
4

Upgrade options

Hardware that runs Nous Dolphin 13B well

👁 NVIDIA
RTX 3090 24GBBudget pick
24 GB VRAM (+8)936 GB/s (+616)
A
Makes the model fit on the accelerator instead of staying completely out of reach.48.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBBest value
24 GB VRAM (+8)1008 GB/s (+688)
A
Makes the model fit on the accelerator instead of staying completely out of reach.56.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade
24 GB VRAM (+8)672 GB/s (+352)
A
Makes the model fit on the accelerator instead of staying completely out of reach.42.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

See all results for NVIDIA T4 16GBSee all hardware for Nous Dolphin 13B
7.3 GB
Medium
A72
Q4_K_M
4
7.9 GB
MediumA72
Q5_K_M
5
9.4 GB
HighA72
Q6_KBest for your GPU
6
10.7 GB
HighA72
Q8_0
8
13.9 GB
Very HighF0
F16
16
26.7 GB
MaximumF0

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.